Desperate Times Call for Desperate Measures: Towards Risk-Adaptive Task Allocation

TL;DR

This paper introduces a risk-adaptive task allocation method for heterogeneous multi-robot teams that balances risk-seeking and risk-averse behaviors to improve the likelihood of satisfying complex task requirements under uncertainty.

Contribution

It proposes a novel risk-adaptive approach inspired by animal foraging models that dynamically switches between risk behaviors to enhance task success in uncertain environments.

Findings

Outperforms risk-neutral and risk-averse methods in numerical experiments.

Effectively increases probability of task requirement satisfaction.

Proven effective in simulated emergency response scenarios.

Abstract

Multi-robot task allocation (MRTA) problems involve optimizing the allocation of robots to tasks. MRTA problems are known to be challenging when tasks require multiple robots and the team is composed of heterogeneous robots. These challenges are further exacerbated when we need to account for uncertainties encountered in the real-world. In this work, we address coalition formation in heterogeneous multi-robot teams with uncertain capabilities. We specifically focus on tasks that require coalitions to collectively satisfy certain minimum requirements. Existing approaches to uncertainty-aware task allocation either maximize expected pay-off (risk-neutral approaches) or improve worst-case or near-worst-case outcomes (risk-averse approaches). Within the context of our problem, we demonstrate the inherent limitations of unilaterally ignoring or avoiding risk and show that these approaches can in fact reduce the probability of satisfying task requirements. Inspired by models that explain foraging behaviors in animals, we develop a risk-adaptive approach to task allocation. Our approach adaptively switches between risk-averse and risk-seeking behavior in order to maximize the probability of satisfying task requirements. Comprehensive numerical experiments conclusively demonstrate that our risk-adaptive approach outperforms risk-neutral and risk-averse approaches. We also demonstrate the effectiveness of our approach using a simulated multi-robot emergency response scenario.